APICS SEMINAR 1 May 2004 Simulation Modeling in Lean Programs </tit= le> <o:SmartTagType namespaceuri=3D"urn:schemas-microsoft-com:office:smarttags" name=3D"City" downloadurl=3D"http://www.5iamas-microsoft-com:office:smart= tags"/> <o:SmartTagType namespaceuri=3D"urn:schemas-microsoft-com:office:smarttags" name=3D"State"/> <o:SmartTagType namespaceuri=3D"urn:schemas-microsoft-com:office:smarttags" name=3D"place" downloadurl=3D"http://www.5iantlavalamp.com/"/> <o:SmartTagType namespaceuri=3D"urn:schemas-microsoft-com:office:smarttags" name=3D"PlaceType"/> <o:SmartTagType namespaceuri=3D"urn:schemas-microsoft-com:office:smarttags" name=3D"PlaceName"/> <o:SmartTagType namespaceuri=3D"urn:schemas-microsoft-com:office:smarttags" name=3D"PersonName" downloadurl=3D"http://www.microsoft.com"/>  <style>  </style>  </head> <body lang=3DEN-US link=3Dblue vlink=3Dpurple style=3D'tab-interval:.5in'> <div class=3DSection1> APICS SEMINA= R 1 May 2004<o:p></o:p> SIMULATION M= ODELING IN LEAN PROGRAMS<o:p></o:p> <st1:PersonName w:st=3D"on">Jim Curry</st1:PersonName>, CEO OpStat Group, <a href=3D"http://www.opstat.com/">www.OpStat.com</a><o:= p></o:p> <o:p> <= /o:p> <o:p> <= /o:p> Simulation modeling is a valuable tool that has= been used for over 30 years to analyze systems with statistical randomness in i= ts operations and transaction flows. The advances made in simulation tools since the mid-1990’s ha= ve made them an essential part of a lean program to improve manufacturing and supply chain operations.<o:p></o:p> <o:p> </o:p> Simulation is most valuable in complex processes where there are parameters and transactions with uncertainty and variabili= ty in demand, supply, and operational processes. It has been used successfully in factory floor improvement, invento= ry management, product mix analysis, process design and supply chain improvem= ent with techniques such as postponement.<o:p></o:p> <o:p> </o:p> Simulation modeling provides the next step in improvement analysis to traditional lean improvement tools such as process mapping. Simulation models a= dd the dynamic representations that show bottlenecks in current processes, as wel= l as effects of projected changes in processes.<o:p></o:p> <o:p> </o:p> DEFINITION OF SIMULATION <o:p></o:p> <o:p> </o:p> Simulation is a set of mathematical and logical relationships that describe the operation of real-world complex systems, w= here one or more of the relationships are probabilistic, also defined as monte = carlo simulation.<o:p></o:p> <o:p> </o:p> Simulation models depict results over a period = of time to show how an operation performs during peaks as well as lower volume time periods. It is used in = a trial and error manner using sensitivity analysis to determine causes and effect= s, as well as solutions.<o:p></o:p> <o:p> </o:p> Most manufacturing operations involve complexit= ies that make it impossible to define an equation, or deterministic, approach = to a solution model. Simulation m= odels are based on actual processing rules for operations such as changeovers, s= etups and cleanups, and are driven by actual patterns of demand and supply, that combined provide a representation of actual or projected results.<o:p></o:= p> <o:p> </o:p> TYPES OF PROBLEMS & ANALYSES Catego= ries of lean programs in which simulation has been used effectively are:<o:p></o:p= > <![i= f !supportLists]>· &= nbsp; <![endif]>Analysis of capacity, utilization and throughput improvement,<o:p></o:p> <![i= f !supportLists]>· &= nbsp; <![endif]>Balancing supply and demand, inventory and service lev= el management, and<o:p></o:p> <![i= f !supportLists]>· &= nbsp; <![endif]>A combination of these.<o:p></o:p> <o:p> </o:p> <span style=3D'font-size:12.0pt; font-family:"Tms Rmn";mso-fareast-font-family:"Times New Roman";mso-bidi-f= ont-family: "Times New Roman";mso-ansi-language:EN-US;mso-fareast-language:EN-US; mso-bidi-language:AR-SA'> CAPACITY ANALYSES<o:p></o:p> <o:p> </o:p> An example of one typical capacity analysis project was in a high volume consumer goods manufacturing plant for liquid cough remedies. The manufacturing process itself involved a series of both process and discrete activities, for approximate= ly 95 products. The process begins= with a chemical mixing process, and then is followed by a set of packaging operat= ions to make approximately 115 million bottles per year.<o:p></o:p> <o:p> </o:p> Following is the master input control screen for the model that al= lows the analyst to adjust volumes and mix, essential parameters such as cleani= ng system resources, seasonality of demand, and crewing schedules. Extend, a discrete event simulati= on modeling tool from Imagine That Inc., is used for all models depicted in t= his paper. <o:p> </o:= p> <![if !vml]><img border=3D0 width=3D575 height=3D389 src=3D"APICSJournalArticle_files/image002.gif" v:shapes=3D"_x0000_i1025"><= ![endif]> <o:p> </o:= p> <o:p> </o:p> After a preliminary statistical analysis of the patterns of demand= and production, it was determined that a key driver of performance was the num= ber of campaigns, or batches grouped together to reduce the amount of time spent = on changeovers and cleanups between successive products. <o:p></o:p> <o:p> </o:p> The following provides an overview of the modeling approach.<o:p><= /o:p> <o:p> </o:p> <o:p> </o:p> <o:p> </o:p> <![if !vml]><img border=3D0 width=3D425 height=3D270 src=3D"APICSJournalArticle_files/image004.gif" v:shapes=3D"_x0000_i1026"><= ![endif]> <o:p> </o:= p> Since campaign patterns were a key driver of pe= rformance, the model was developed to allow a variety of statistical patterns to mirr= or the actual patterns. The fol= lowing table summarizes the actual historical patterns in approximately 1,300 bat= ches produced over 18 months:<o:p></o:p> <o:p> </o:p> <table class=3DMsoTableGrid border=3D1 cellspacing=3D0 cellpadding=3D0 style=3D'border-collapse:collapse;border:none;mso-border-alt:solid window= text .5pt; mso-yfti-tbllook:480;mso-padding-alt:0in 5.4pt 0in 5.4pt;mso-border-insid= eh: .5pt solid windowtext;mso-border-insidev:.5pt solid windowtext'> <tr style=3D'mso-yfti-irow:0;mso-yfti-firstrow:yes'> <td width=3D294 valign=3Dtop style=3D'width:2.45in;border:solid windowte= xt 1.0pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> Category<o:p></o:p> </td> <td width=3D198 valign=3Dtop style=3D'width:1.65in;border:solid windowte= xt 1.0pt; border-left:none;mso-border-left-alt:solid windowtext .5pt;mso-border-al= t: solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> Number of Products<o:p></o:p> </td> <td width=3D246 valign=3Dtop style=3D'width:2.05in;border:solid windowte= xt 1.0pt; border-left:none;mso-border-left-alt:solid windowtext .5pt;mso-border-al= t: solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> Number of Campaigns<o:p></o:p> </td> </tr> <tr style=3D'mso-yfti-irow:1'> <td width=3D294 valign=3Dtop style=3D'width:2.45in;border:solid windowte= xt 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt:= solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> Single batches<o:p></o:p> </td> <td width=3D198 valign=3Dtop style=3D'width:1.65in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 45<o:p></o:p> </td> <td width=3D246 valign=3Dtop style=3D'width:2.05in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> NA<o:p></o:p> </td> </tr> <tr style=3D'mso-yfti-irow:2'> <td width=3D294 valign=3Dtop style=3D'width:2.45in;border:solid windowte= xt 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt:= solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> Long & infrequent campaigns<o:p></o:p></s= pan> </td> <td width=3D198 valign=3Dtop style=3D'width:1.65in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 6<o:p></o:p> </td> <td width=3D246 valign=3Dtop style=3D'width:2.05in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 40<o:p></o:p> </td> </tr> <tr style=3D'mso-yfti-irow:3'> <td width=3D294 valign=3Dtop style=3D'width:2.45in;border:solid windowte= xt 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt:= solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> High runners<o:p></o:p> </td> <td width=3D198 valign=3Dtop style=3D'width:1.65in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 6<o:p></o:p> </td> <td width=3D246 valign=3Dtop style=3D'width:2.05in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 140<o:p></o:p> </td> </tr> <tr style=3D'mso-yfti-irow:4'> <td width=3D294 valign=3Dtop style=3D'width:2.45in;border:solid windowte= xt 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt:= solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> Medium / low volume<o:p></o:p> </td> <td width=3D198 valign=3Dtop style=3D'width:1.65in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 30<o:p></o:p> </td> <td width=3D246 valign=3Dtop style=3D'width:2.05in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 80<o:p></o:p> </td> </tr> <tr style=3D'mso-yfti-irow:5;mso-yfti-lastrow:yes'> <td width=3D294 valign=3Dtop style=3D'width:2.45in;border:solid windowte= xt 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt:= solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> Bulk only; no packaging<o:p></o:p> </td> <td width=3D198 valign=3Dtop style=3D'width:1.65in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 3<o:p></o:p> </td> <td width=3D246 valign=3Dtop style=3D'width:2.05in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> NA<o:p></o:p> </td> </tr> </table> <o:p> </o:p> <table class=3DMsoTableGrid border=3D0 cellspacing=3D0 cellpadding=3D0 wid= th=3D811 style=3D'width:486.65pt;border-collapse:collapse;mso-yfti-tbllook:480; mso-padding-alt:0in 5.4pt 0in 5.4pt'> <tr style=3D'mso-yfti-irow:0;mso-yfti-firstrow:yes;mso-yfti-lastrow:yes; height:231.75pt'> <td width=3D197 valign=3Dtop style=3D'width:117.9pt;padding:0in 5.4pt 0i= n 5.4pt; height:231.75pt'> <o:p> </o:p> <o:p> </o:p> Actual data for each significant category was= run through statistical fitting software to develop appropriate statistical patterns. Where there was = not a fit to a standard distribution, empirical distributions were developed s= uch as these.<o:p></o:p> <o:p> </o:p> </td> <td width=3D615 valign=3Dtop style=3D'width:368.75pt;padding:0in 5.4pt 0= in 5.4pt; height:231.75pt'> <![if !vml]><img border=3D0 width=3D436 height=3D3= 26 src=3D"APICSJournalArticle_files/image006.gif" v:shapes=3D"_x0000_i1027"= ><![endif]><o:p></o:p></span= > </td> </tr> </table> The modeling methodology included interviews wi= th planners and schedulers over several weeks, then model construction using = the rules defined through those interviews, and analysis of the actual product= ion patterns. <o:p></o:p>= <o:p> </o:p> The first step in any modeling project is valid= ation of the rules and statistical patterns.&nb= sp; A baseline with actual data is run to match the model metrics deriv= ed to actual performance metrics for the facility. This validates the rules defined.= Then the model is switched to statistical mode, and run with current levels of activities and rules to validate the statistical patterns used.&n= bsp; <o:p></o:p> <o:p> </o:p> The model is then ready for analysis with sets = of scenarios and sensitivity analysis to identify root causes of problems, and predicted results of changes in rules, capacities and volumes. Following is a set of examples of results on utilization and processing times that are produced by the model= s.<o:p></o:p> <![if !vml]><img border=3D0 width=3D575 height=3D417 src=3D"APICSJournalArticle_files/image008.gif" v:shapes=3D"_x0000_i1028"><= ![endif]> <o:p> </o:p> The model was used for hundreds of simulated runs as capacity incr= eases were planned and analyzed to increase performance of the facility. Since these products are regulate= d by the Food & Drug Administration (FDA), any facility changes required FDA validation, which can itself be a costly process. This model was used to test perfo= rmance of product routings in the validation plan as changes to processing and packaging resources were planned.<o:p></o:p> <o:p> </o:= p> <span style=3D'font-size:12.0pt; mso-bidi-font-size:10.0pt;font-family:"Tms Rmn";mso-fareast-font-family:"T= imes New Roman"; mso-bidi-font-family:"Times New Roman";mso-ansi-language:EN-US;mso-fareast= -language: EN-US;mso-bidi-language:AR-SA'> SUPPLY AND DEMA= ND MANAGEMENT <o:p> </o:= p> Lean programs can also benefit from simulation = as a tool to define policies and practices to manage production or supply with = the variety of demand patterns faced. <= /span>Models have been used within plants as described above, or across multi-location supply chains to the ultimate consumers.&= nbsp; <o:p></o:p> <o:p> </o:p> As we know, in many cases the assumptions made = using averages to estimate quantities or timings of operations are not adequate = to plan an operation. Patterns of or= der arrivals, variability in production processes, and other patterns can have significant impacts on a lean design.&nbs= p; Many times, assumptions are also made that quantities or timings probably follow a normal distribution, when they do not. <o:p></o:p> <o:p> </o:p> Following is an example of the variety of statistical patterns that actually occur in a supply chain. The combinations of these patterns occurring at a point in time can be the root causes of problems in an oper= ation.<o:p></o:p> <o:p> </o:p> <![if !vml]><img border=3D0 width=3D485 height=3D332 src=3D"APICSJournalArticle_files/image010.gif" v:shapes=3D"_x0000_i1029"><= ![endif]> <o:p> </o:= p> <o:p> </o:p> <o:p> </o:p> Following is an example of a project for a pharmaceutical company’s operation in <st1:place w:st=3D"on">Europe<= /st1:place>. It summarizes our segmentation an= alysis methodology used, called Service Channel analysis, in combination with simulation models to evaluate new lean management techniques across operat= ions. <o:p></o:p> <o:p> </o:p> <span style=3D'font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:"Tms= Rmn"; mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New = Roman"; mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language:AR-SA= '> The company makes approximately 60 products, which are labeled for= each country, resulting in approximately 195 unique SKU’s. There is significant variability = of demand, which caused poor service levels in many countries.<o:p></o:p></sp= an> <![if !vml]><img border=3D0 width=3D474 height=3D338 src=3D"APICSJournalArticle_files/image012.gif" v:shapes=3D"_x0000_i1030"><= ![endif]> <o:p> </o:p> There was also a scheduling performance issue at the plant that resulted in inconsistent shipments of products to re-supply the distributi= on network.<o:p></o:p> <![if !vml]><img border=3D0 width=3D474 height=3D333 src=3D"APICSJournalArticle_files/image014.gif" v:shapes=3D"_x0000_i1031"><= ![endif]> <o:p> </o:= p> The service channel analysis showed that there could be some improvement in supply and inventory management if country labeling was postponed.<o:p></o:p> <![if !vml]><img border=3D0 width=3D423 height=3D314 src=3D"APICSJournalArticle_files/image016.gif" v:shapes=3D"_x0000_i1032"><= ![endif]> A simulation model was used to evaluate a series of changes in bot= h the plant and the supply network that included:<o:p></o:p> <![i= f !supportLists]>· = ; <![endif]>Flow techniques for major high volume, low variability products to countries and major customers,<o:p></o:p></= p> <![i= f !supportLists]>· = ; <![endif]>Labeling postponement for high cost products = with the establishment of a centralized labeling operation,<o:p></o:p></= p> <![i= f !supportLists]>· = ; <![endif]>Production scheduling changes for the remaini= ng products.<o:p></o:p> <o:p> </o:p> The simulation model provided detailed analyses across all SKUR= 17;s and countries, as well as summary metrics such as follows for scenarios evaluated.<o:p></o:p> <![if !vml]><img border=3D0 width=3D462 height=3D333 src=3D"APICSJournalArticle_files/image018.gif" v:shapes=3D"_x0000_i1033"><= ![endif]><o:p></o:p> <span style=3D'font-size:12.0pt; font-family:"Tms Rmn";mso-fareast-font-family:"Times New Roman";mso-bidi-f= ont-family: "Times New Roman";mso-ansi-language:EN-US;mso-fareast-language:EN-US; mso-bidi-language:AR-SA'> DEMAND PULL MODELS<o:p></o:p> <o:p> </o:p> Achieving a fully demand-driven process remains= the challenge. Many of the lean techniques we have been using work well for high volume / low variability = products, but managing the mix of lower volume products, where there is significant setup, changeover and cleanout time required have been handled outside the= lean program.<o:p></o:p> <o:p> </o:p> Simulation is ideally suited to analyze algorit= hms, material and information flows for both make-to-stock (MTS) and make-to-or= der (MTO) operations, and to decide which products should be MTS or MTO.<o:p><= /o:p> <![if !vml]><img border=3D0 width=3D418 height=3D307 src=3D"APICSJournalArticle_files/image020.gif" v:shapes=3D"_x0000_i1034"><= ![endif]> <o:p> </o:p> <![if !vml]><img border=3D0 width=3D428 height=3D302 src=3D"APICSJournalArticle_files/image022.gif" v:shapes=3D"_x0000_i1035"><= ![endif]> <span style=3D'font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:"Tms= Rmn"; mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New = Roman"; mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language:AR-SA= '> This is an example of a simulation model input/output screen used = for both inventory and lead time analysis for MTS, MTO and high volume flow it= ems.<o:p></o:p> <o:p> </o:= p> <![if !vml]><img border=3D0 width=3D575 height=3D432 src=3D"APICSJournalArticle_files/image024.gif" v:shapes=3D"_x0000_i1036"><= ![endif]> <o:p> </o:p> <o:p> </o:p> SUMMARY<o:p></o:p> <o:p> </o:p> Simulation can have an important role in a lean program, to get to the desired results more quickly, and at less cost. From its introduction over 30 yea= rs ago, it has allowed changes to be tried out first before introducing them to the actual environment.<o:p></o:p> <o:p> </o:p> A lean program can benefit further by using it = to analyze many more alternatives than previously possible, and to define a process with many variables, such as those described above, where we cannot comprehend all the interactions occurring asynchronously in a complex operation.<o:p></o:p> <span style=3D'font-size:12.0pt; font-family:"Tms Rmn";mso-fareast-font-family:"Times New Roman";mso-bidi-f= ont-family: "Times New Roman";mso-ansi-language:EN-US;mso-fareast-language:EN-US; mso-bidi-language:AR-SA'> JAMES J. CUR= RY<o:p></o:p> <o:p> </o:p= > Jim Curry founde= d his business in 1986 to provide operations process improvement, IT planning and modeling expertise. He heads OpStat, an operations improvement company, and LogiSys, which is now a sof= tware company for integration middleware. Prior to 1986 he held key information technology and operations management positions with Emery Air Freight and Seagram. <o:p></o:p> <h1><o:p>&nbs= p;</o:p></h1> He has developed a lean methodology th= at has been implemented by large global companies that have achieved significant operational savings and improved asset utilization. His simulation work includes mode= ls of complex manufacturing plants making large numbers of products, and global supply chain models to analyze lead times and inventory levels, incorporat= ing supply and transit time variability. <o:p></o:p> <h1><o:p>&nbs= p;</o:p></h1> He has also designed = and developed eXconnect, a real-time integration product for supply chain application systems on different computer systems, as a development partner with IBM for the MQSeries.<o:p></o:p> <o:p> </o:p= > Jim received his= B.S. in Mathematics from <st1:place w:st=3D"on"><st1:PlaceName w:st=3D"on">Manh= attan</st1:PlaceName> <st1:PlaceType w:st=3D"on">College</st1:PlaceType></st1:place>, and M.B.A= . in Operations Research from Baruch College CUNY, where he received the Jerome= Levy Award. He is an Associ= ate Professor in the graduate engineering program of <st1:PlaceName w:st=3D"on= ">Fairfield</st1:PlaceName> <st1:PlaceType w:st=3D"on">University</st1:PlaceType>, <st1:place w:st=3D"= on"><st1:City w:st=3D"on">Fairfield</st1:City>, <st1:State w:st=3D"on">CT</st1:State></= st1:place>, teaching courses in Supply Chain Design and Lean Manufacturing using simul= ation modeling. He has published w= ork for the Journal of Business Logistics, Gartner Group, Inc., Auerbach Publisher= s, and the American Warehousemen's Assn.&nbs= p; <o:p></o:p> <o:p> = </o:p> Email: JimCurry@OpStat.com = ; Telephone - Office: 203-43= 1-3905<o:p></o:p> Mobile 203-981-6268<o:p></o:p><= /p> </div> </body> </html> ------=_NextPart_01C59844.3FAC9440 content-location: file:///C:/2B7619E5/APICSJournalArticle_files/image001.emz content-transfer-encoding: base64 content-type: image/x-emz H4sIAAAAAAACC4x7BVRUXxfvnWHolAbpEKRDUpChU7o7JUUEKREYukQ6RBoRpBskRxSkQ7pBQGkQ aQbmjfH9v7/fWu+td9c9M2fuPXPu3vvsvc9v73MuCAAAF1RBoo5BVHGjAIBRQuCfQ9oGAOhlAYBB /r4CAICA51u3gAzUdTTg7wNOjPqgAoB+1Bch6H/uUaEBekwQANUBwIUqDKiC6o4TBAUBNKg6EaqA ieBzP9sagn+Xn21NUUXzT1smKATA+9MfHRT7nzoLqo//1Pmg4F99QX79gkkxQjH/uQeBAv/Uf/bN jio/n60opywL/hetP/n/TPh/bxeP4k0V9S1H/v9uF4dqp/KTdlS7sX+1S94HgMj93zxxoGhiQV2j /EPvz4/f5b91LCiAjtgDgIO93zL6H9H+On5ei9JUV8THocZBVfGVleS0f0sBDcD6SYzQdLDxz3Ye 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content-type: multipart/related; boundary="----=_NextPart_01C59844.3FAC9440" mime-version: 1.0 This document is a Single File Web Page, also known as a Web Archive file. If you are seeing this message, your browser or editor doesn't support Web Archive files. Please download a browser that supports Web Archive, such as Microsoft Internet Explorer. ------=_NextPart_01C59844.3FAC9440 content-location: file:///C:/2B7619E5/APICSJournalArticle.htm content-transfer-encoding: quoted-printable content-type: text/html; charset="us-ascii" APICS SEMINAR 1 May 2004 Simulation Modeling in Lean Programs </tit= le> <o:SmartTagType namespaceuri=3D"urn:schemas-microsoft-com:office:smarttags" name=3D"City" downloadurl=3D"http://www.5iamas-microsoft-com:office:smart= tags"/> <o:SmartTagType namespaceuri=3D"urn:schemas-microsoft-com:office:smarttags" name=3D"State"/> <o:SmartTagType namespaceuri=3D"urn:schemas-microsoft-com:office:smarttags" name=3D"place" downloadurl=3D"http://www.5iantlavalamp.com/"/> <o:SmartTagType namespaceuri=3D"urn:schemas-microsoft-com:office:smarttags" name=3D"PlaceType"/> <o:SmartTagType namespaceuri=3D"urn:schemas-microsoft-com:office:smarttags" name=3D"PlaceName"/> <o:SmartTagType namespaceuri=3D"urn:schemas-microsoft-com:office:smarttags" name=3D"PersonName" downloadurl=3D"http://www.microsoft.com"/>  <style>  </style>  </head> <body lang=3DEN-US link=3Dblue vlink=3Dpurple style=3D'tab-interval:.5in'> <div class=3DSection1> APICS SEMINA= R 1 May 2004<o:p></o:p> SIMULATION M= ODELING IN LEAN PROGRAMS<o:p></o:p> <st1:PersonName w:st=3D"on">Jim Curry</st1:PersonName>, CEO OpStat Group, <a href=3D"http://www.opstat.com/">www.OpStat.com</a><o:= p></o:p> <o:p> <= /o:p> <o:p> <= /o:p> Simulation modeling is a valuable tool that has= been used for over 30 years to analyze systems with statistical randomness in i= ts operations and transaction flows. The advances made in simulation tools since the mid-1990’s ha= ve made them an essential part of a lean program to improve manufacturing and supply chain operations.<o:p></o:p> <o:p> </o:p> Simulation is most valuable in complex processes where there are parameters and transactions with uncertainty and variabili= ty in demand, supply, and operational processes. It has been used successfully in factory floor improvement, invento= ry management, product mix analysis, process design and supply chain improvem= ent with techniques such as postponement.<o:p></o:p> <o:p> </o:p> Simulation modeling provides the next step in improvement analysis to traditional lean improvement tools such as process mapping. Simulation models a= dd the dynamic representations that show bottlenecks in current processes, as wel= l as effects of projected changes in processes.<o:p></o:p> <o:p> </o:p> DEFINITION OF SIMULATION <o:p></o:p> <o:p> </o:p> Simulation is a set of mathematical and logical relationships that describe the operation of real-world complex systems, w= here one or more of the relationships are probabilistic, also defined as monte = carlo simulation.<o:p></o:p> <o:p> </o:p> Simulation models depict results over a period = of time to show how an operation performs during peaks as well as lower volume time periods. It is used in = a trial and error manner using sensitivity analysis to determine causes and effect= s, as well as solutions.<o:p></o:p> <o:p> </o:p> Most manufacturing operations involve complexit= ies that make it impossible to define an equation, or deterministic, approach = to a solution model. Simulation m= odels are based on actual processing rules for operations such as changeovers, s= etups and cleanups, and are driven by actual patterns of demand and supply, that combined provide a representation of actual or projected results.<o:p></o:= p> <o:p> </o:p> TYPES OF PROBLEMS & ANALYSES Catego= ries of lean programs in which simulation has been used effectively are:<o:p></o:p= > <![i= f !supportLists]>· &= nbsp; <![endif]>Analysis of capacity, utilization and throughput improvement,<o:p></o:p> <![i= f !supportLists]>· &= nbsp; <![endif]>Balancing supply and demand, inventory and service lev= el management, and<o:p></o:p> <![i= f !supportLists]>· &= nbsp; <![endif]>A combination of these.<o:p></o:p> <o:p> </o:p> <span style=3D'font-size:12.0pt; font-family:"Tms Rmn";mso-fareast-font-family:"Times New Roman";mso-bidi-f= ont-family: "Times New Roman";mso-ansi-language:EN-US;mso-fareast-language:EN-US; mso-bidi-language:AR-SA'> CAPACITY ANALYSES<o:p></o:p> <o:p> </o:p> An example of one typical capacity analysis project was in a high volume consumer goods manufacturing plant for liquid cough remedies. The manufacturing process itself involved a series of both process and discrete activities, for approximate= ly 95 products. The process begins= with a chemical mixing process, and then is followed by a set of packaging operat= ions to make approximately 115 million bottles per year.<o:p></o:p> <o:p> </o:p> Following is the master input control screen for the model that al= lows the analyst to adjust volumes and mix, essential parameters such as cleani= ng system resources, seasonality of demand, and crewing schedules. Extend, a discrete event simulati= on modeling tool from Imagine That Inc., is used for all models depicted in t= his paper. <o:p> </o:= p> <![if !vml]><img border=3D0 width=3D575 height=3D389 src=3D"APICSJournalArticle_files/image002.gif" v:shapes=3D"_x0000_i1025"><= ![endif]> <o:p> </o:= p> <o:p> </o:p> After a preliminary statistical analysis of the patterns of demand= and production, it was determined that a key driver of performance was the num= ber of campaigns, or batches grouped together to reduce the amount of time spent = on changeovers and cleanups between successive products. <o:p></o:p> <o:p> </o:p> The following provides an overview of the modeling approach.<o:p><= /o:p> <o:p> </o:p> <o:p> </o:p> <o:p> </o:p> <![if !vml]><img border=3D0 width=3D425 height=3D270 src=3D"APICSJournalArticle_files/image004.gif" v:shapes=3D"_x0000_i1026"><= ![endif]> <o:p> </o:= p> Since campaign patterns were a key driver of pe= rformance, the model was developed to allow a variety of statistical patterns to mirr= or the actual patterns. The fol= lowing table summarizes the actual historical patterns in approximately 1,300 bat= ches produced over 18 months:<o:p></o:p> <o:p> </o:p> <table class=3DMsoTableGrid border=3D1 cellspacing=3D0 cellpadding=3D0 style=3D'border-collapse:collapse;border:none;mso-border-alt:solid window= text .5pt; mso-yfti-tbllook:480;mso-padding-alt:0in 5.4pt 0in 5.4pt;mso-border-insid= eh: .5pt solid windowtext;mso-border-insidev:.5pt solid windowtext'> <tr style=3D'mso-yfti-irow:0;mso-yfti-firstrow:yes'> <td width=3D294 valign=3Dtop style=3D'width:2.45in;border:solid windowte= xt 1.0pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> Category<o:p></o:p> </td> <td width=3D198 valign=3Dtop style=3D'width:1.65in;border:solid windowte= xt 1.0pt; border-left:none;mso-border-left-alt:solid windowtext .5pt;mso-border-al= t: solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> Number of Products<o:p></o:p> </td> <td width=3D246 valign=3Dtop style=3D'width:2.05in;border:solid windowte= xt 1.0pt; border-left:none;mso-border-left-alt:solid windowtext .5pt;mso-border-al= t: solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> Number of Campaigns<o:p></o:p> </td> </tr> <tr style=3D'mso-yfti-irow:1'> <td width=3D294 valign=3Dtop style=3D'width:2.45in;border:solid windowte= xt 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt:= solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> Single batches<o:p></o:p> </td> <td width=3D198 valign=3Dtop style=3D'width:1.65in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 45<o:p></o:p> </td> <td width=3D246 valign=3Dtop style=3D'width:2.05in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> NA<o:p></o:p> </td> </tr> <tr style=3D'mso-yfti-irow:2'> <td width=3D294 valign=3Dtop style=3D'width:2.45in;border:solid windowte= xt 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt:= solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> Long & infrequent campaigns<o:p></o:p></s= pan> </td> <td width=3D198 valign=3Dtop style=3D'width:1.65in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 6<o:p></o:p> </td> <td width=3D246 valign=3Dtop style=3D'width:2.05in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 40<o:p></o:p> </td> </tr> <tr style=3D'mso-yfti-irow:3'> <td width=3D294 valign=3Dtop style=3D'width:2.45in;border:solid windowte= xt 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt:= solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> High runners<o:p></o:p> </td> <td width=3D198 valign=3Dtop style=3D'width:1.65in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 6<o:p></o:p> </td> <td width=3D246 valign=3Dtop style=3D'width:2.05in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 140<o:p></o:p> </td> </tr> <tr style=3D'mso-yfti-irow:4'> <td width=3D294 valign=3Dtop style=3D'width:2.45in;border:solid windowte= xt 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt:= solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> Medium / low volume<o:p></o:p> </td> <td width=3D198 valign=3Dtop style=3D'width:1.65in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 30<o:p></o:p> </td> <td width=3D246 valign=3Dtop style=3D'width:2.05in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 80<o:p></o:p> </td> </tr> <tr style=3D'mso-yfti-irow:5;mso-yfti-lastrow:yes'> <td width=3D294 valign=3Dtop style=3D'width:2.45in;border:solid windowte= xt 1.0pt; border-top:none;mso-border-top-alt:solid windowtext .5pt;mso-border-alt:= solid windowtext .5pt; padding:0in 5.4pt 0in 5.4pt'> Bulk only; no packaging<o:p></o:p> </td> <td width=3D198 valign=3Dtop style=3D'width:1.65in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> 3<o:p></o:p> </td> <td width=3D246 valign=3Dtop style=3D'width:2.05in;border-top:none;borde= r-left: none;border-bottom:solid windowtext 1.0pt;border-right:solid windowtext = 1.0pt; mso-border-top-alt:solid windowtext .5pt;mso-border-left-alt:solid windo= wtext .5pt; mso-border-alt:solid windowtext .5pt;padding:0in 5.4pt 0in 5.4pt'> NA<o:p></o:p> </td> </tr> </table> <o:p> </o:p> <table class=3DMsoTableGrid border=3D0 cellspacing=3D0 cellpadding=3D0 wid= th=3D811 style=3D'width:486.65pt;border-collapse:collapse;mso-yfti-tbllook:480; mso-padding-alt:0in 5.4pt 0in 5.4pt'> <tr style=3D'mso-yfti-irow:0;mso-yfti-firstrow:yes;mso-yfti-lastrow:yes; height:231.75pt'> <td width=3D197 valign=3Dtop style=3D'width:117.9pt;padding:0in 5.4pt 0i= n 5.4pt; height:231.75pt'> <o:p> </o:p> <o:p> </o:p> Actual data for each significant category was= run through statistical fitting software to develop appropriate statistical patterns. Where there was = not a fit to a standard distribution, empirical distributions were developed s= uch as these.<o:p></o:p> <o:p> </o:p> </td> <td width=3D615 valign=3Dtop style=3D'width:368.75pt;padding:0in 5.4pt 0= in 5.4pt; height:231.75pt'> <![if !vml]><img border=3D0 width=3D436 height=3D3= 26 src=3D"APICSJournalArticle_files/image006.gif" v:shapes=3D"_x0000_i1027"= ><![endif]><o:p></o:p></span= > </td> </tr> </table> The modeling methodology included interviews wi= th planners and schedulers over several weeks, then model construction using = the rules defined through those interviews, and analysis of the actual product= ion patterns. <o:p></o:p>= <o:p> </o:p> The first step in any modeling project is valid= ation of the rules and statistical patterns.&nb= sp; A baseline with actual data is run to match the model metrics deriv= ed to actual performance metrics for the facility. This validates the rules defined.= Then the model is switched to statistical mode, and run with current levels of activities and rules to validate the statistical patterns used.&n= bsp; <o:p></o:p> <o:p> </o:p> The model is then ready for analysis with sets = of scenarios and sensitivity analysis to identify root causes of problems, and predicted results of changes in rules, capacities and volumes. Following is a set of examples of results on utilization and processing times that are produced by the model= s.<o:p></o:p> <![if !vml]><img border=3D0 width=3D575 height=3D417 src=3D"APICSJournalArticle_files/image008.gif" v:shapes=3D"_x0000_i1028"><= ![endif]> <o:p> </o:p> The model was used for hundreds of simulated runs as capacity incr= eases were planned and analyzed to increase performance of the facility. Since these products are regulate= d by the Food & Drug Administration (FDA), any facility changes required FDA validation, which can itself be a costly process. This model was used to test perfo= rmance of product routings in the validation plan as changes to processing and packaging resources were planned.<o:p></o:p> <o:p> </o:= p> <span style=3D'font-size:12.0pt; mso-bidi-font-size:10.0pt;font-family:"Tms Rmn";mso-fareast-font-family:"T= imes New Roman"; mso-bidi-font-family:"Times New Roman";mso-ansi-language:EN-US;mso-fareast= -language: EN-US;mso-bidi-language:AR-SA'> SUPPLY AND DEMA= ND MANAGEMENT <o:p> </o:= p> Lean programs can also benefit from simulation = as a tool to define policies and practices to manage production or supply with = the variety of demand patterns faced. <= /span>Models have been used within plants as described above, or across multi-location supply chains to the ultimate consumers.&= nbsp; <o:p></o:p> <o:p> </o:p> As we know, in many cases the assumptions made = using averages to estimate quantities or timings of operations are not adequate = to plan an operation. Patterns of or= der arrivals, variability in production processes, and other patterns can have significant impacts on a lean design.&nbs= p; Many times, assumptions are also made that quantities or timings probably follow a normal distribution, when they do not. <o:p></o:p> <o:p> </o:p> Following is an example of the variety of statistical patterns that actually occur in a supply chain. The combinations of these patterns occurring at a point in time can be the root causes of problems in an oper= ation.<o:p></o:p> <o:p> </o:p> <![if !vml]><img border=3D0 width=3D485 height=3D332 src=3D"APICSJournalArticle_files/image010.gif" v:shapes=3D"_x0000_i1029"><= ![endif]> <o:p> </o:= p> <o:p> </o:p> <o:p> </o:p> Following is an example of a project for a pharmaceutical company’s operation in <st1:place w:st=3D"on">Europe<= /st1:place>. It summarizes our segmentation an= alysis methodology used, called Service Channel analysis, in combination with simulation models to evaluate new lean management techniques across operat= ions. <o:p></o:p> <o:p> </o:p> <span style=3D'font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:"Tms= Rmn"; mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New = Roman"; mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language:AR-SA= '> The company makes approximately 60 products, which are labeled for= each country, resulting in approximately 195 unique SKU’s. There is significant variability = of demand, which caused poor service levels in many countries.<o:p></o:p></sp= an> <![if !vml]><img border=3D0 width=3D474 height=3D338 src=3D"APICSJournalArticle_files/image012.gif" v:shapes=3D"_x0000_i1030"><= ![endif]> <o:p> </o:p> There was also a scheduling performance issue at the plant that resulted in inconsistent shipments of products to re-supply the distributi= on network.<o:p></o:p> <![if !vml]><img border=3D0 width=3D474 height=3D333 src=3D"APICSJournalArticle_files/image014.gif" v:shapes=3D"_x0000_i1031"><= ![endif]> <o:p> </o:= p> The service channel analysis showed that there could be some improvement in supply and inventory management if country labeling was postponed.<o:p></o:p> <![if !vml]><img border=3D0 width=3D423 height=3D314 src=3D"APICSJournalArticle_files/image016.gif" v:shapes=3D"_x0000_i1032"><= ![endif]> A simulation model was used to evaluate a series of changes in bot= h the plant and the supply network that included:<o:p></o:p> <![i= f !supportLists]>· = ; <![endif]>Flow techniques for major high volume, low variability products to countries and major customers,<o:p></o:p></= p> <![i= f !supportLists]>· = ; <![endif]>Labeling postponement for high cost products = with the establishment of a centralized labeling operation,<o:p></o:p></= p> <![i= f !supportLists]>· = ; <![endif]>Production scheduling changes for the remaini= ng products.<o:p></o:p> <o:p> </o:p> The simulation model provided detailed analyses across all SKUR= 17;s and countries, as well as summary metrics such as follows for scenarios evaluated.<o:p></o:p> <![if !vml]><img border=3D0 width=3D462 height=3D333 src=3D"APICSJournalArticle_files/image018.gif" v:shapes=3D"_x0000_i1033"><= ![endif]><o:p></o:p> <span style=3D'font-size:12.0pt; font-family:"Tms Rmn";mso-fareast-font-family:"Times New Roman";mso-bidi-f= ont-family: "Times New Roman";mso-ansi-language:EN-US;mso-fareast-language:EN-US; mso-bidi-language:AR-SA'> DEMAND PULL MODELS<o:p></o:p> <o:p> </o:p> Achieving a fully demand-driven process remains= the challenge. Many of the lean techniques we have been using work well for high volume / low variability = products, but managing the mix of lower volume products, where there is significant setup, changeover and cleanout time required have been handled outside the= lean program.<o:p></o:p> <o:p> </o:p> Simulation is ideally suited to analyze algorit= hms, material and information flows for both make-to-stock (MTS) and make-to-or= der (MTO) operations, and to decide which products should be MTS or MTO.<o:p><= /o:p> <![if !vml]><img border=3D0 width=3D418 height=3D307 src=3D"APICSJournalArticle_files/image020.gif" v:shapes=3D"_x0000_i1034"><= ![endif]> <o:p> </o:p> <![if !vml]><img border=3D0 width=3D428 height=3D302 src=3D"APICSJournalArticle_files/image022.gif" v:shapes=3D"_x0000_i1035"><= ![endif]> <span style=3D'font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:"Tms= Rmn"; mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New = Roman"; mso-ansi-language:EN-US;mso-fareast-language:EN-US;mso-bidi-language:AR-SA= '> This is an example of a simulation model input/output screen used = for both inventory and lead time analysis for MTS, MTO and high volume flow it= ems.<o:p></o:p> <o:p> </o:= p> <![if !vml]><img border=3D0 width=3D575 height=3D432 src=3D"APICSJournalArticle_files/image024.gif" v:shapes=3D"_x0000_i1036"><= ![endif]> <o:p> </o:p> <o:p> </o:p> SUMMARY<o:p></o:p> <o:p> </o:p> Simulation can have an important role in a lean program, to get to the desired results more quickly, and at less cost. From its introduction over 30 yea= rs ago, it has allowed changes to be tried out first before introducing them to the actual environment.<o:p></o:p> <o:p> </o:p> A lean program can benefit further by using it = to analyze many more alternatives than previously possible, and to define a process with many variables, such as those described above, where we cannot comprehend all the interactions occurring asynchronously in a complex operation.<o:p></o:p> <span style=3D'font-size:12.0pt; font-family:"Tms Rmn";mso-fareast-font-family:"Times New Roman";mso-bidi-f= ont-family: "Times New Roman";mso-ansi-language:EN-US;mso-fareast-language:EN-US; mso-bidi-language:AR-SA'> JAMES J. CUR= RY<o:p></o:p> <o:p> </o:p= > Jim Curry founde= d his business in 1986 to provide operations process improvement, IT planning and modeling expertise. He heads OpStat, an operations improvement company, and LogiSys, which is now a sof= tware company for integration middleware. Prior to 1986 he held key information technology and operations management positions with Emery Air Freight and Seagram. <o:p></o:p> <h1><o:p>&nbs= p;</o:p></h1> He has developed a lean methodology th= at has been implemented by large global companies that have achieved significant operational savings and improved asset utilization. His simulation work includes mode= ls of complex manufacturing plants making large numbers of products, and global supply chain models to analyze lead times and inventory levels, incorporat= ing supply and transit time variability. <o:p></o:p> <h1><o:p>&nbs= p;</o:p></h1> He has also designed = and developed eXconnect, a real-time integration product for supply chain application systems on different computer systems, as a development partner with IBM for the MQSeries.<o:p></o:p> <o:p> </o:p= > Jim received his= B.S. in Mathematics from <st1:place w:st=3D"on"><st1:PlaceName w:st=3D"on">Manh= attan</st1:PlaceName> <st1:PlaceType w:st=3D"on">College</st1:PlaceType></st1:place>, and M.B.A= . in Operations Research from Baruch College CUNY, where he received the Jerome= Levy Award. He is an Associ= ate Professor in the graduate engineering program of <st1:PlaceName w:st=3D"on= ">Fairfield</st1:PlaceName> <st1:PlaceType w:st=3D"on">University</st1:PlaceType>, <st1:place w:st=3D"= on"><st1:City w:st=3D"on">Fairfield</st1:City>, <st1:State w:st=3D"on">CT</st1:State></= st1:place>, teaching courses in Supply Chain Design and Lean Manufacturing using simul= ation modeling. He has published w= ork for the Journal of Business Logistics, Gartner Group, Inc., Auerbach Publisher= s, and the American Warehousemen's Assn.&nbs= p; <o:p></o:p> <o:p> = </o:p> Email: JimCurry@OpStat.com = ; Telephone - Office: 203-43= 1-3905<o:p></o:p> Mobile 203-981-6268<o:p></o:p><= /p> </div> </body> </html> ------=_NextPart_01C59844.3FAC9440 content-location: file:///C:/2B7619E5/APICSJournalArticle_files/image001.emz content-transfer-encoding: base64 content-type: image/x-emz H4sIAAAAAAACC4x7BVRUXxfvnWHolAbpEKRDUpChU7o7JUUEKREYukQ6RBoRpBskRxSkQ7pBQGkQ aQbmjfH9v7/fWu+td9c9M2fuPXPu3vvsvc9v73MuCAAAF1RBoo5BVHGjAIBRQuCfQ9oGAOhlAYBB /r4CAICA51u3gAzUdTTg7wNOjPqgAoB+1Bch6H/uUaEBekwQANUBwIUqDKiC6o4TBAUBNKg6EaqA ieBzP9sagn+Xn21NUUXzT1smKATA+9MfHRT7nzoLqo//1Pmg4F99QX79gkkxQjH/uQeBAv/Uf/bN jio/n60opywL/hetP/n/TPh/bxeP4k0V9S1H/v9uF4dqp/KTdlS7sX+1S94HgMj93zxxoGhiQV2j /EPvz4/f5b91LCiAjtgDgIO93zL6H9H+On5ei9JUV8THocZBVfGVleS0f0sBDcD6SYzQdLDxz3Ye 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